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DOE OSTI2026

From natural language to control signals: a conceptual framework for semantic channel finding in complex experimental infrastructure

Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.

channel finding↗

NASA NTRS2021

3D Cloud Tomography and Droplet Size Retrieval from Multi-Angle Polarimetric Imaging of Scattered Sunlight from Above

Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging,it is extensively used for diagnostics via X-ray computed tomography (CT). We define and derive a tomographyof cloud droplet distributions via passive remote sensing. We use multi-view polarimetric images to fit a 3Dpolarized radiative transfer (RT) forward model. Our motivation is 3D volumetric probing of vertically-developedconvectively-driven clouds that are ill-served by current methods in operational passive remote sensing. Currenttechniques are indeed based on strictly 1D RT modeling and applied to a single cloudy pixel, where cloud geometrydefaults to that of a plane-parallel slab. Incident unpolarized sunlight, once scattered by cloud droplets, changesits polarization state according to droplet size. Therefore, polarimetric measurements in the rainbow and gloryangular regions can be used to infer the droplet size distribution. This work defines and derives a framework for afull 3D tomography of cloud droplets for both their mass concentration in space and their distribution across arange of sizes. This gridded 3D retrieval of key microphysical properties is made tractable by our novel approachthat involves a restructuring and partial linearization of an open-source polarized 3D RT code to accommodate aspecial two-step iterative optimization technique. Physically-realistic synthetic clouds are used to demonstrate themethodology with rigorous uncertainty quantification, while a real-world cloud imaged by AirMSPI is processedto illustrate the new remote sensing capability

Schechner, Yoav Y.↗

DOE OSTI2026

Deep-ultraviolet ptychographic pocket-scope (DART): mesoscale lensless molecular imaging with label-free spectroscopic contrast

The mesoscale characterization of biological specimens has traditionally required compromises between resolution, field-of-view, depth-of-field, and molecular specificity, with most approaches relying on external labels. Here we present the Deep-ultrAviolet ptychogRaphic pockeT-scope (DART), a handheld platform that transforms label-free molecular imaging through intrinsic deep-ultraviolet spectroscopic contrast. By leveraging biomolecules’ natural absorption fingerprints and combining them with lensless ptychographic microscopy, DART resolves down to 308-nm linewidths across centimeter-scale areas while maintaining millimeter-scale depth-of-field. The system’s virtual error-bin methodology effectively eliminates artifacts from limited temporal coherence and other optical imperfections, enabling high-fidelity molecular imaging without lenses. Through differential spectroscopic imaging at deep-ultraviolet wavelengths, DART quantitatively maps nucleic acid and protein distributions with femtogram sensitivity, providing an intrinsic basis for explainable virtual staining. We demonstrate DART’s capabilities through imaging of tissue sections, cytopathology specimens, blood cells, and neural populations, revealing detailed molecular contrast without external labels. The combination of high-resolution molecular mapping and broad mesoscale imaging in a portable platform opens new possibilities from rapid clinical diagnostics, tissue analysis, to biological characterization in space exploration.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2001

Far-Infrared Extragalactic Surveys: Past, Present, and Future

As much as one third of the luminosity of the local universe is emitted in the far infrared. In order to understand the history of energy release in the universe, it is crucial to characterize this rest-frame far-infrared contribution from the present back to the era of initial galaxy formation. Over the redshift range from 0 to 10, this energy is received in the 80 micrometers to 1 mm spectral region. In the 1980's the Infrared Astronomy Satellite (IRAS) all-sky survey provided the first comprehensive view of the far infrared emission from the local universe. The diffuse background measurements by Cosmic Background Explorer Satellite (COBE) have provided constraints on the integral contributions from the high redshift universe. In the past five years, submillimeter measurements made using the SCUBA instrument have revealed powerful high redshift sources. To develop a clear history of energy release in the universe, we need numbers and redshifts of representative populations of energetically important objects. The near future will bring the Space Infrared Telescope Facility Multiband Imaging Photometer (SIRTF)(MIPS) survey, which will cover about 100 square degrees at wavelengths out to 160 micrometers, providing a large sample of energetically important galaxies out to z of approx.3. In 2005, the Japanese IRIS survey will provide a 160 micrometers full sky survey, which will provide larger samples of the high z galaxy populations and will find intrinsically rare high luminosity objects. The SPIRE instrument on the FIRST facility will extend these surveys to longer wavelengths, providing a view of the universe at higher redshifts in three spectral bands. A concept for an all-sky submillimeter survey is under development, called the Survey of Infrared Cosmic Evolution (SIRCE). With a 2 m cryogenic telescope, it can map the entire sky to the confusion limit in the 100 to 500 micrometers range in six months. This survey will provide photometric redshifts, number counts, and will find the most luminous objects in the universe. In the next decade, the opening of the submillimeter, combined with the near infrared capability of NGST will provide us with a clear picture of energy release in the early universe.

Moseley, Samuel H., Jr.↗

DOE OSTI2025

ORBITaL-Net: A labeled training library for large-scale building feature extraction

Over the course of several years, nearly 1.5 million building outlines have been created from approximately 128,000 training tiles covering roughly 7,000 km 2 of very high-resolution multispectral overhead imagery, primarily dated between 2010 and 2020. This dataset, dubbed the Oak Ridge Building Image and TrAining Label Net (ORBITaL-Net), is designed for machine learning applications and is global in scope, with samples drawn from 72 countries across North America, South America, Africa, Europe, and Asia. ORBITaL-Net captures a great diversity in geographic setting, structural characteristics, land use (urban and rural), terrain, and imagery conditions. While the labeled building outlines are themselves valuable, the dataset’s true strength lies in the pairing of these labels with corresponding reference imagery, which is being released for open source use. Similar to SpaceNet and Replicable AI For Microplanning (ramp), this building outline dataset will allow the larger computer vision community from academia, government, and industry the opportunity to develop robust, scalable, and generalizable geospatial machine learning techniques. Unlike SpaceNet and ramp, which offer high resolution labels and imagery primarily for large urban cities, ORBITaL-Net is not focused on training samples from heavily populated areas but instead aims to capture the innate variability of conditions present in both the physical environment and imagery collections.

Geography↗

NASA NTRSDate not supplied

Enabling Model Organism and Commercial Astronaut Data Access Through the NASA Open Science Data Repository

NASA’s Open Science Data Repository (OSDR) brings together omics data from NASA’s GeneLab project and non-omics data, including physiological, phenotypic, imaging, and behavioral data from NASA’s Ames Life Sciences Data Archive (ALSDA) collected from decades of space biology research, providing open and FAIR (findable, accessible, interoperable, and reusable) access of these precious data to scientists world-wide. This rich source of meticulously curated metadata and data from spaceflight and analog studies has been mined by the scientific community resulting in dozens of high impact scientific publications that reveals a complex network of molecular and physiological effects of spaceflight across living systems, from microbes to plants, to mammals. Understanding how these effects translate to the human condition is critical as we move deeper into the era of commercial space travel. However, the integration of data, specifically omics data, from astronauts is particularly challenging due to their sensitive nature. OSDR has risen to this challenge by developing a mechanism to control access to identifiable levels of omics data, such as raw sequence data, while enabling public access to processed, unidentifiable, data and associated metadata that will allow the scientific community to interrogate human astronaut data alongside data from model organisms to begin answering these critical questions. The 2021 SpaceX Inspiration4 (I4) mission collected a comprehensive atlas of biological measurements from four civilian astronauts, providing a wealth of data to characterize the effects of spaceflight on the human body. These data include both non-omics and omics assays such as direct RNA sequencing (RNA-seq), single nuclei ATAC-seq and RNA-seq, metagenomics, proteomics, and comprehensive metabolic and cytokine panels, all of which have been integrated into the OSDR system across no less than 9 studies. Each study has been carefully curated using community-backed OSDR standards for sample and assay level metadata ensuring these data are findable and accessible. In addition to hosting both raw and processed data from the principal investigator team for each assay type, the GeneLab team plans to re-process the I4 omics data using GeneLab’s standard processing pipelines. The GeneLab processed data outputs will allow for comparisons across studies on OSDR and enable visualization of these data through the OSDR data visualization platform thereby enabling data reusability and interoperability. Here we describe the robust privacy and security protocols implemented by OSDR to safeguard sensitive health data from astronauts while facilitating metadata and processed data sharing for research purposes. We further provide a road map for navigating the vast amount of data provided for each I4 study on the OSDR, including experimental design, associated experiments, payloads, and missions, data generation and analysis protocols, and associated scientific articles. Additionally, we illustrate how to interrogate the standardized metadata provided in the sample and assay tables as well as various means to download and access the data including programmatically through the GeneLab Open API (GLOpenAPI). The open access of datasets in NASA’s OSDR provides a unique opportunity for the scientific community, as well as citizen scientists and students, to continue using OSDR resources to further unlock profound insights into the consequences of space travel on the human body. Through implementation of security measures to protect sensitive human data, the OSDR seeks to strengthen the science exchange between the Biological and Physical Sciences Program and the Human Research Program, per recommendation 4-1 of the 2023-2032 Decadal Survey, and encourage further sharing and dissemination of astronaut data to provide the scientific community with the resources needed to lay the groundwork for developing targeted mitigation strategies to help withstand the rigors of long-duration spaceflight.

Amanda Marie Saravia-butler↗

NASA NTRS2013

The HERschel Inventory of the Agents of Galaxy Evolution in the Magellanic Clouds, a HERschel Open Time Key Program

We present an overview or the HERschel Inventory of The Agents of Galaxy Evolution (HERITAGE) in the Magellanic Clouds project, which is a Herschel Space Observatory open time key program. We mapped the Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC) at 100, 160, 250, 350, and 500 micron with the Spectral and Photometric Imaging Receiver (SPIRE) and Photodetector Array Camera and Spectrometer (PACS) instruments on board Herschel using the SPIRE/PACS parallel mode. The overriding science goal of HERITAGE is to study the life cycle of matter as traced by dust in the LMC and SMC. The far-infrared and submillimeter emission is an effective tracer of the interstellar medium (ISM) dust, the most deeply embedded young stellar objects (YSOs), and the dust ejected by the most massive stars. We describe in detail the data processing, particularly for the PACS data, which required some custom steps because of the large angular extent of a single observational unit and overall the large amount of data to be processed as an ensemble. We report total global fluxes for LMC and SMC and demonstrate their agreement with measurements by prior missions. The HERITAGE maps of the LMC and SMC are dominated by the ISM dust emission and bear most resemblance to the tracers of ISM gas rather than the stellar content of the galaxies. We describe the point source extraction processing and the critetia used to establish a catalog for each waveband for the HERITAGE program. The 250 micron band is the most sensitive and the source catalogs for this band have approx. 25,000 objects for the LMC and approx. 5500 objects for the SMC. These data enable studies of ISM dust properties, submillimeter excess dust emission, dust-to-gas ratio, Class 0 YSO candidates, dusty massive evolved stars, supemova remnants (including SN1987A), H II regions, and dust evolution in the LMC and SMC. All images and catalogs are delivered to the Herschel Science Center as part of the conummity support aspects of the project. These HERITAGE images and catalogs provide an excellent basis for future research and follow up with other facilities.

AGENTS↗

DOE OSTI2025

Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties

This project developed machine learning (ML) methods, lab data sets, and field data to advance geothermal exploration and geothermal energy production. The work had three focus areas. One involved the development of ML methods to use microearthquakes (MEQs) for imaging geothermal reservoir properties and improving subsurface characterization – most importantly the evolution of permeability within the evolving reservoir. This part of the work included development of ML approaches for automated MEQ location, focal mechanism determination and identification of earthquake precursors. The second area focused on using MEQ signals generated by geothermal exploration and production to predict the relationship between fluid injection and seismicity. Here, we extended to reservoir scale our success in using ML to predict laboratory earthquakes and fault zone stress state. The third focus area was on lab experiments. Here, we developed new ML models for lab earthquake prediction and identification of precursors to failure to improve earthquake forecasting and early warning in geothermal settings. Major outcomes of our work include ML models that learn from MEQ signals during geothermal exploration and production to predict induced seismicity. MEQs occur naturally in connection with drilling and energy production. We developed ML methods to use the seismic waves from these events to characterize the elastic, hydraulic and poromechanical properties of reservoirs. Our work illuminated fracture geometry and the evolution of fracture permeability by incorporating seismic coda wave analysis and ML methods to relate fluid injection and seismicity. We significantly expanded laboratory earthquake prediction to include methods that use both passive measurements of microearthquakes within the lab fault zones and also active source acoustic measurements of fault zone elastic properties. These methods can now predict fault zone stress state, time to failure and the magnitude of lab earthquakes. Our work showed that repetitive stick- slip failure events during frictional sliding (the lab equivalent of earthquakes) are preceded by a cascade of micro-failure events that radiate energy in a manner that foretells unstable failure – manifest as laboratory MEQs. We documented a mapping between fracture properties and statistical attributes of elastic radiation. We extended existing works to geothermal reservoir scale and developed ML methods to determine reservoir permeability, fracture properties, and their evolution during geothermal energy production. An attractive feature of ML algorithms is their ability to handle big datasets and reveal patterns and correlations that may remain invisible to conventional analyses. Our work connected data from field, laboratory and intermediate scales to study permeability, stress, strength, fracture stiffness and geometry. At the field scale we used data from the Newberry Volcano field site, UtahFORGE, EGS Collab, and also the Bedretto underground research lab in Switzerland. These data sets are bridging the gap between the lab scale, theory, and reservoir scale. Our work produced plain language summaries to improve public understanding of DOE research. We also developed openly distributed ML and seismicity datasets for use by all researchers and we published connections between induced seismicity in geothermal areas and reservoir properties including permeability, fracture properties, and stress state. Our models are designed for the large data sets of induced seismicity typically associated with geothermal sites. We produced labeled event catalogs and used them on geothermal data to assess how ML can facilitate geothermal production and exploration. All datasets are available on the GDR Productivity: The project produced 32 publications in peer reviewed journals (two are in review). It supported the work of 6 PhD students, 40 conference presentations, 6 keynote talks at national meetings, and mentoring and professional development for 4 postdoctoral fellows.

15 GEOTHERMAL ENERGY↗

NASA NTRS2006

Data Integration Support for Data Served in the OPeNDAP and OGC Environments

NASA is coordinating a technology development project to construct a gateway between system components built upon the Open-source Project for a Network Data AcceSs Protocol (OPeNDAP) and those made available made available via interfaces specified by the Open Geospatial Consortium (OGC). This project is funded though the Advanced Collaborative Connections for Earth-Sun System Science (ACCESS) Program and is a NASA contribution to the Committee on Earth Satellites (CEOS) Working Group on Information Systems and Services (WGISS). The motivation for the project is the set of data integration needs that have been expressed by the Coordinated Enhanced Observing Period (CEOP), an international program that is addressing the study of the global water cycle. CEOP is assembling a large collection in situ and satellite data and mode1 results from a wide variety of sources covering 35 sites around the globe. The data are provided by systems based on either the OPeNDAP or OGC protocols but the research community desires access to the full range of data and associated services from a single client. This presentation will discuss the current status of the OPeNDAP/OGC Gateway Project. The project is building upon an early prototype that illustrated the feasibility of such a gateway and which was demonstrated to the CEOP science community. In its first year as an ACCESS project, the effort has been has focused on the design of the catalog and data services that will be provided by the gateway and the mappings between the metadata and services provided in the two environments.

McDonald, Kenneth R.↗

NASA NTRS2010

On Simulating the Impacts of Open Water Bodies on the SMAP Passive Soil Moisture Data Product

The Soil Moisture Active and Passive (SMAP) mission is a NASA earth science mission aiming at improving our understanding of the dynamics of the cycles of energy, water, and carbon at global scales. The mission features two complementary sensors on the same low-Earth orbiting platform: an L-band synthetic aperture radar (SAR) operating at 1.26 GHz and an L-band radiometer operating at 1.41 GHz. Together these instruments will provide global mapping of soil moisture and freeze/thaw states in 2-3 days, with a tentative launch date in 2014. The work reported in this study focuses primarily on the development of the SMAP radiometer-only soil moisture data product. For passive soil moisture retrieval at satellite footprint scales, one way to improve retrieval accuracy is to correct for the microwave emission from open water bodies prior to retrieval. The accuracy of this correction will depend on not only the locations of these water bodies, but also the geolocation accuracy of the instrument. As perfect knowledge is never attainable in practice, it is important to assess the impacts of these uncertainties on the SMAP radiometer observations and hence the passive soil moisture retrieval accuracy. In this presentation, we present the results of our preliminary assessment on the impacts of these uncertainties. Our study consists of two parts: (1) a sensitivity analysis on the SMAP radiometer observations due to uncertainties in water-body classification, and (2) realistic global simulations that take into account of additional uncertainties (e.g., geolocation and ancillary data) and SMAP-specific instrument characteristics (e.g., orbit sampling and antenna pattern). The results will provide valuable prelaunch guidance to the SMAP team in identifying different error sources and their relative impacts on the passive soil moisture data product.

Chan, Steven↗

NASA NTRS2010

Proposed Use of the NASA Ames Nebula Cloud Computing Platform for Numerical Weather Prediction and the Distribution of High Resolution Satellite Imagery

The development of the Nebula Cloud Computing Platform at NASA Ames Research Center provides an open-source solution for the deployment of scalable computing and storage capabilities relevant to the execution of real-time weather forecasts and the distribution of high resolution satellite data to the operational weather community. Two projects at Marshall Space Flight Center may benefit from use of the Nebula system. The NASA Short-term Prediction Research and Transition (SPoRT) Center facilitates the use of unique NASA satellite data and research capabilities in the operational weather community by providing datasets relevant to numerical weather prediction, and satellite data sets useful in weather analysis. SERVIR provides satellite data products for decision support, emphasizing environmental threats such as wildfires, floods, landslides, and other hazards, with interests in numerical weather prediction in support of disaster response. The Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS) has been configured for Nebula cloud computing use via the creation of a disk image and deployment of repeated instances. Given the available infrastructure within Nebula and the "infrastructure as a service" concept, the system appears well-suited for the rapid deployment of additional forecast models over different domains, in response to real-time research applications or disaster response. Future investigations into Nebula capabilities will focus on the development of a web mapping server and load balancing configuration to support the distribution of high resolution satellite data sets to users within the National Weather Service and international partners of SERVIR.

Limaye, Ashutosh S.↗

NASA NTRS2017

UAV Inspection of Electrical Transmission Infrastructure with Path Conformance Autonomy and Lidar-Based Geofences NASA Report on UTM Reference Mission Flights at Southern Company Flights November 2016

Flights at low altitudes in close proximity to electrical transmission infrastructure present serious navigational challenges: GPS and radio communication quality is variable and yet tight position control is needed to measure defects while avoiding collisions with ground structures. To advance unmanned aerial vehicle (UAV) navigation technology while accomplishing a task with economic and societal benefit, a high voltage electrical infrastructure inspection reference mission was designed. An integrated air-ground platform was developed for this mission and tested in two days of experimental flights to determine whether navigational augmentation was needed to successfully conduct a controlled inspection experiment. The airborne component of the platform was a multirotor UAV built from commercial off-the-shelf hardware and software, and the ground component was a commercial laptop running open source software. A compact ultraviolet sensor mounted on the UAV can locate 'hot spots' (potential failure points in the electric grid), so long as the UAV flight path adequately samples the airspace near the power grid structures. To improve navigation, the platform was supplemented with two navigation technologies: lidar-to-polyhedron preflight processing for obstacle demarcation and inspection distance planning, and trajectory management software to enforce inspection standoff distance. Both navigation technologies were essential to obtaining useful results from the hot spot sensor in this obstacle-rich, low-altitude airspace. Because the electrical grid extends into crowded airspaces, the UAV position was tracked with NASA unmanned aerial system traffic management (UTM) technology. The following results were obtained: (1) Inspection of high-voltage electrical transmission infrastructure to locate 'hot spots' of ultraviolet emission requires navigation methods that are not broadly available and are not needed at higher altitude flights above ground structures. (2) The sensing capability of a novel airborne UV detector was verified with a standard ground-based instrument. Flights with this sensor showed that UAV measurement operations and recording methods are viable. With improved sensor range, UAVs equipped with compact UV sensors could serve as the detection elements in a self-diagnosing power grid. (3) Simplification of rich lidar maps to polyhedral obstacle maps reduces data volume by orders of magnitude, so that computation with the resultant maps in real time is possible. This enables real-time obstacle avoidance autonomy. Stable navigation may be feasible in the GPS-deprived environment near transmission lines by a UAV that senses ground structures and compares them to these simplified maps. (4) A new, formally verified path conformance software system that runs onboard a UAV was demonstrated in flight for the first time. It successfully maneuvered the aircraft after a sudden lateral perturbation that models a gust of wind, and processed lidar-derived polyhedral obstacle maps in real time. (5) Tracking of the UAV in the national airspace using the NASA UTM technology was a key safety component of this reference mission, since the flights were conducted beneath the landing approach to a heavily used runway. Comparison to autopilot tracking showed that UTM tracking accurately records the UAV position throughout the flight path.

Moore, Andrew J.↗

NASA NTRS2016

The Matsu Wheel: A Cloud-Based Framework for Efficient Analysis and Reanalysis of Earth Satellite Imagery

Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for cloud-based processing of Earth satellite imagery with practical applications to aid in natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce and related technologies. We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel." The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework allows batches of analytics, scanning for new data, to be applied to data as it flows in. In the Matsu Wheel, the data only need to be accessed and preprocessed once, regardless of the number or types of analytics, which can easily be slotted into the existing framework. The Matsu Wheel system provides a significantly more efficient use of computational resources over alternative methods when the data are large, have high-volume throughput, may require heavy preprocessing, and are typically used for many types of analysis. We also describe our preliminary Wheel analytics, including an anomaly detector for rare spectral signatures or thermal anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. Each of these analytics can generate visual reports accessible via the web for the public and interested decision makers. The result products of the analytics are also made accessible through an Open Geospatial Compliant (OGC)-compliant Web Map Service (WMS) for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.

0↗

NASA NTRS2021

Evaluation of Global Surface Water Temperature Data Sets for use in Passive Remote Sensing of Soil Moisture

Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.

Runze Zhang↗

NASA NTRS2011

Seafloor Control on Sea Ice

The seafloor has a profound role in Arctic sea ice formation and seasonal evolution. Ocean bathymetry controls the distribution and mixing of warm and cold waters, which may originate from different sources, thereby dictating the pattern of sea ice on the ocean surface. Sea ice dynamics, forced by surface winds, are also guided by seafloor features in preferential directions. Here, satellite mapping of sea ice together with buoy measurements are used to reveal the bathymetric control on sea ice growth and dynamics. Bathymetric effects on sea ice formation are clearly observed in the conformation between sea ice patterns and bathymetric characteristics in the peripheral seas. Beyond local features, bathymetric control appears over extensive ice-prone regions across the Arctic Ocean. The large-scale conformation between bathymetry and patterns of different synoptic sea ice classes, including seasonal and perennial sea ice, is identified. An implication of the bathymetric influence is that the maximum extent of the total sea ice cover is relatively stable, as observed by scatterometer data in the decade of the 2000s, while the minimum ice extent has decreased drastically. Because of the geologic control, the sea ice cover can expand only as far as it reaches the seashore, the continental shelf break, or other pronounced bathymetric features in the peripheral seas. Since the seafloor does not change significantly for decades or centuries, sea ice patterns can be recurrent around certain bathymetric features, which, once identified, may help improve short-term forecast and seasonal outlook of the sea ice cover. Moreover, the seafloor can indirectly influence cloud cover by its control on sea ice distribution, which differentially modulates the latent heat flux through ice covered and open water areas.

Nghiem, S. V.↗

NASA NTRSDate not supplied

Hail Storm Risk Assessment Using Space-Borne Remote Sensing Observations and Reanalyses

Much of the world is impacted by severe thunderstorms, but whether they become disasters depends upon resilience--our capacity to prepare, mitigate, respond, and recover. Hail is the costliest severe weather hazard for the insurance industry, generating ~70% of severe convective storm losses due to damage to assets such as homes, businesses, agriculture, and infrastructure. Most insurance companies do not reserve enough capital to cover catastrophes, so they acquire reinsurance. The reinsurance industry uses catastrophe models (CatModels) to statistically estimate risk to an insurer’s portfolio. Hail CatModels are developed with climatologies that define hailstorm frequency and severity. Hail-prone areas can be defined using hail reports from trained spotters, the media, and the general public. Extremely severe hail (2+ inch diameter) occurs nearly every day across the world. Weather radars can detect hail because hailstones strongly reflect microwave signals that they emit. However, hail climatologies are difficult to derive because hail covers small areas and there are neither hail reporting mechanisms (e.g. website or mobile app) nor radar networks in most places outside the US and Europe. This lack of ground truth on severe hail puts society and economies at risk. Hail is generated within storms by strong updrafts. These updrafts exhibit unique signatures in NASA and other agency satellite observations, offering new opportunities for hailstorm analysis. Geostationary (GEO) visible and infrared imagery has been collected for ~15-25 years across the world (region dependent) and methods have been developed at NASA Langley Research Center (LaRC) to detect hailstorm updrafts using GEO imagery. Climatological GEO updraft data has been used by Willis Towers Watson (WTW), a leader in catastrophe risk assessment for the insurance industry, and Karlsruhe Institute of Technology to develop CatModels over Europe and Australia. Hail can also be inferred with passive microwave imagery collected by low-Earth-orbiting sensors such as the GPM GMI, TRMM TMI, AMSR-E, AMSR-2, SSM/I, and SSMIS over the last 20+ years using methods developed at the Marshall Space Flight Center (MSFC). Hailstorms generate enhanced lightning flash rates that can be tracked using new GOES-R series GEO Lightning Mapping (GLM) imagery. Atmospheric reanalyses can be used to define favorable hailstorm environments for combination with the satellite-based storm detections. This presentation will describe a framework for developing continental to global hail climatologies and CatModels based on NASA satellite data and capabilities. This is a collaboration between LaRC and MSFC, WTW, and partners in Brazil, Argentina, and South Africa. This project seeks to mitigate hail disasters by aiding development of new satellite-based severe storm nowcasting tools by regional partners and developing climatologies to improve societal understanding of hail frequency. GEOO visible and infrared metrics of storm intensity, environmental conditions based on reanalyses, spotter hail reports and radar MESH observations are intercompared to quantify the detectability of hailstorms, and our ability to discriminate hailstorms from other severe storms. We are also maturing methods using land surface imaging satellite data (e.g. MODIS, Landsat, Sentinel 1 and 2) to identify hail damage to agriculture. Work with WTW will improve socioeconomic resilience through development of new CatModels. Southern Brazil, Uruguay, Paraguay, and Argentina feature some of the most intense thunderstorms on Earth. South America and South Africa are developing insurance markets of interest to WTW clients, and is similar to other regions routinely impacted by hail that do not have comprehensive hail reporting or radars to assess hailstorm frequency. Project datasets will be made available via online GIS-enabled tools developed at the LaRC Atmospheric Science Data Center (ASDC) which will visualize data and provide it in multiple formats for use in a wide range of open source and commercial tools.

Kristopher Michael Bedka↗

NASA NTRS2008

Standardizing Interfaces for External Access to Data and Processing for the NASA Ozone Product Evaluation and Test Element (PEATE)

NASA's traditional science data processing systems have focused on specific missions, and providing data access, processing and services to the funded science teams of those specific missions. Recently NASA has been modifying this stance, changing the focus from Missions to Measurements. Where a specific Mission has a discrete beginning and end, the Measurement considers long term data continuity across multiple missions. Total Column Ozone, a critical measurement of atmospheric composition, has been monitored for'decades on a series of Total Ozone Mapping Spectrometer (TOMS) instruments. Some important European missions also monitor ozone, including the Global Ozone Monitoring Experiment (GOME) and SCIAMACHY. With the U.S.IEuropean cooperative launch of the Dutch Ozone Monitoring Instrument (OMI) on NASA Aura satellite, and the GOME-2 instrumental on MetOp, the ozone monitoring record has been further extended. In conjunction with the U.S. Department of Defense (DoD) and the National Oceanic and Atmospheric Administration (NOAA), NASA is now preparing to evaluate data and algorithms for the next generation Ozone Mapping and Profiler Suite (OMPS) which will launch on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) in 2010. NASA is constructing the Science Data Segment (SDS) which is comprised of several elements to evaluate the various NPP data products and algorithms. The NPP SDS Ozone Product Evaluation and Test Element (PEATE) will build on the heritage of the TOMS and OM1 mission based processing systems. The overall measurement based system that will encompass these efforts is the Atmospheric Composition Processing System (ACPS). We have extended the system to include access to publically available data sets from other instruments where feasible, including non-NASA missions as appropriate. The heritage system was largely monolithic providing a very controlled processing flow from data.ingest of satellite data to the ultimate archive of specific operational data products. The ACPS allows more open access with standard protocols including HTTP, SOAPIXML, RSS and various REST incarnations. External entities can be granted access to various modules within the system, including an extended data archive, metadata searching, production planning and processing. Data access is provided with very fine grained access control. It is possible to easily designate certain datasets as being available to the public, or restricted to groups of researchers, or limited strictly to the originator. This can be used, for example, to release one's best validated data to the public, but restrict the "new version" of data processed with a new, unproven algorithm until it is ready. Similarly, the system can provide access to algorithms, both as modifiable source code (where possible) and fully integrated executable Algorithm Plugin Packages (APPs). This enables researchers to download publically released versions of the processing algorithms and easily reproduce the processing remotely, while interacting with the ACPS. The algorithms can be modified allowing better experimentation and rapid improvement. The modified algorithms can be easily integrated back into the production system for large scale bulk processing to evaluate improvements. The system includes complete provenance tracking of algorithms, data and the entire processing environment. The origin of any data or algorithms is recorded and the entire history of the processing chains are stored such that a researcher can understand the entire data flow. Provenance is captured in a form suitable for the system to guarantee scientific reproducability of any data product it distributes even in cases where the physical data products themselves have been deleted due to space constraints. We are currently working on Semantic Web ontologies for representing the various provenance information. A new web site focusing on consolidating informaon about the measurement, processing system, and data access has been established to encourage interaction with the overall scientific community. We will describe the system, its data processing capabilities, and the methods the community can use to interact with the standard interfaces of the system.

Tilmes, Curt A.↗

DOE OSTI2026

From 2D to 4D: a containerized workflow and browser to explore dynamic chromatin architecture

Background Characterizing the physical organization of the genome is essential for understanding long-range gene regulation, chromatin compartmentalization, and epigenetic accessibility. Hi-C experiments generate two-dimensional (2D) genome-wide contact maps of chromatin interactions by capturing the spatial proximity between genomic loci, which reveal interaction frequencies but lack the spatial resolution needed to interpret the three-dimensional (3D) genome structure(s). Emerging evidence suggests that epigenetic regulation is closely linked to 3D genome architecture, and that structural changes over time (4D) drive key biological processes in development, disease, and environmental response. Thus, integrating 3D structure with functional data is critical for a more complete understanding of genome regulation. Previous work, most notably the 4DHiC chromosome modeling framework, has shown that physical multi-dimensional modeling approaches rooted in polymer physics and molecular dynamics can resolve these structures at biologically meaningful resolutions by integrating temporal Hi-C data with physical constraints to uncover dynamic chromosome reorganization. Thus, molecular dynamics simulations, constrained by Hi-C contact matrices, can resolve fine-scale structural changes and reveal functionally significant transitions in chromatin conformation. Results Herein, we present the 4D Genome Browser Workflow (4DGBWorkflow) and the 4D Genome Browser (4DGB). The algorithm is based on the 4DHiC method, and the containerized tool is an end-to-end workflow that can transform, filter, and view 4D epigenomics and chromatin datasets, allowing non-specialists to apply three-dimensional modeling principles to diverse datasets and experimental conditions. The software executes on a laptop running macOS, Linux or Windows. From input Hi-C files (.hic), the 4DGBWorkflow produces 3D reconstructions of chromosomes, integrates the reconstruction with track data (e.g., epigenetic marks, transcriptome profiles), and provides comparative visualization of the results in a single workflow. Conclusions The 4DGBWorkflow and 4D Genome Browser are open-source tools for comparative analysis and visualization of 4D chromosome datasets, including chromatin architecture and epigenomic signals. Automatic integration of Hi-C data with molecular dynamics democratizes the construction of time resolved 3D genome structures, simplifying complex simulations and data integration schemes.

3D Genome Browser↗